Introduction and Background Information
Research Question and Introduction
What is the relationship between socioeconomic indicators of
livability and environmental quality in different areas of Valencia,
Spain?
Aim of Investigation
Urban livability is defined as the wellbeing and quality of life of
inhabitants in a given urban area according to both social and economic
indicators (Martino, N., Girling, C. and Lu, Y., 2021). It can
be measured by looking at 4 main indicators which include accessibility,
social diversity, affordability, and economic vitality. This concept is
specifically relevant for urban planning policies focusing on
facilitating access to diversity while remaining affordable in urban
environments. For example, Valencia has implemented several urban
planning policies, specifically focusing on the importance of green
spaces to improve the city’s livability like the Green Infrastructure
Plan of Valencia (Plaverd Valencia, 2024). This investigation
is therefore significant to understand how the level of urban livability
changes depending on the environmental quality and varying levels of
green space. This will help confirm and understand the significance of
urban planning policies for green space in Valencia.
Geographical Context
As shown by Fig.1, Spain is located in southwestern Europe, sharing a
border with France and Portugal. Fig.2 shows how Spain is divided into
17 autonomous communities (regions), one of them being Valencia located
on the eastern coast of the country (Britannica, 2019). This
investigation focuses on the city of Valencia, part of the region of
Valencia, which is an urban area composed of 19 districts. Valencia is
an interesting location to carry out an investigation on urban
environment given it is the 3th largest city of Spain with a population
of 814 208 residents in 2024 (World population review,
2024).
Background Information
Urban livability has a complex and multi-dimensional nature, making
urban livability difficult to define and measure. We can therefore look
at 4 different indicators to better understand the concept of
livability.
Accessibility
Accessibility can be defined in two different ways. It is either the
relative accessibility between two points, measured by the time taken to
reach key destinations, or total accessibility at a point, measured by
the proximity of that point to necessary services (Ingram, D.R.,
1971), (Gaglione, F., Gargiulo, C., Zucaro, F. and Cottrill,
C., 2022). Otherwise, accessibility can also be measured by looking
at public transport frequency or pedestrian and cyclist counts
(Martino, N., Girling, C. and Lu, Y., 2021).
Accessibility will increase livability as it will increase life
satisfaction of city dwellers, specifically the travel satisfaction when
reaching their daily destinations outside their residential areas
(Zhan, D., Kwan, M.-P., Zhang, W., Fan, J., Yu, J. and Dang, Y.,
2018). In addition, studies have shown that people in car-dependent
neighborhoods often have a lower quality of life owing to higher levels
of pollution and congestion (Frank, L.d, 2009). Hence, areas
with active transportation infrastructure like sidewalks or bike lanes
or with higher level of connectivity through public transport, will be
prone to higher levels of accessibility and quality of life and
satisfaction, increasing urban livability.
Social Diversity
Social diversity is defined as the variety of people in an area,
including differences in income levels, ethnicities, or age group
(Talen, E. and Lee, S., 2018). Social Diversity contributes to
urban livability as it enhances economic vitality by promoting
creativity among individuals and businesses. Exposure to diverse
backgrounds, experiences, and perspectives will stimulate innovation and
boost the overall creative and innovative capacity of human capital
(Moroni, S., 2016).
Affordability
Affordability is defined as whether or not the residential land in a
given area is financially affordable for a majority of citizens. It
specifically looks at housing cost as a proportion of an individual’s
disposable income (OECD, 2022). Another aspect of affordability is
looking at the cost of life for city dwellers. The more affordable
amenities are, such as hospitals, schools, or public transit systems the
greater the access to infrastructures related to health, education, or
modern transportation which help ensure a greater quality of life among
the urbanites (Abdul Shakur, E.S., Mohamed, A.F. and Abdul Hadi,
A.S., 2017).
Economic Vitality
Economic vitality is the overall health and strength of a community’s
economy. It can be achieved through prosperous local businesses, job
opportunities, and access to essential services that allow residents to
live comfortably and achieve economic and social well being.
Environmental Quality
Environmental Quality is a relative concept, based on comparison to
given criterion, which can vary over time. Because of its
multidimensional nature, environmental quality can be measured
considering both quantitative and qualitative dimensions (Lawrence,
R.J, 2014). An environmental quality survey can be conducted by an
observer to assess environmental quality of the urban environment
against a range of indicators. Each indicator is graded on a scale of
quality, usually from 1 to 5, to represent less good to good (Field
Studies Council, 2023).
Hypothesis
Hypothesis 1- With higher income brackets, indicators of
livability generally increase, except for affordability and potentially
diversity.
Accessibility would hypothetically increase with income as urban
areas in higher income brackets tend to have better infrastructure,
public transport, and walkability. This idea can be supported by the
Monocentric City Model which assumes that an urban area is designed
around one main center known as the central business district (CBD) and
spread outward in layers of various land use (Abozeid, A.S.M. and
AboElatta, T.A., 2021). The CBD is the commercial and economic core
of a city and is therefore characterized by office buildings and retail
stores. Hence, accessibility is both a cause and consequence of the
Monocentric City Model. Cities historically developed around a single
core due to accessibility advantages. Over time, transportation systems
like major roads and public transport rail networks were constructed
into a radial and monocentric system that converged toward the center
(The Geography of Transport Systems, 2017). Hence, in many
cases today, the CBD acts as the main transport hub of an urban area and
accessibility naturally declines with distance from the CBD. This is
also reinforced by the fact that commercial and retail land use in the
CBD attracts a large workforce, necessitating efficient transport links
to accommodate daily commutes. This theory can be applied to Valencia
which shows characteristics of a Monocentric City with commercial and
administrative land use concentrated in the center in districts like
Ciutat Vella and L’Eixample. However, Ciudad de las Artes y las Ciencias
located in Quatre Carreres is also an important economic and cultural
area of the city which demonstrates the polycentric tendencies of
Valencia and this could cause some variation in the data collected for
accessibility.
Social Diversity could potentially increase or decrease with income
bracket. One of the factors that could influence the social diversity in
different districts of Valencia is the level of gentrification.
Gentrification is the process of reinvesting money into deprived
inner-city areas to make neighbourhoods more attractive, leading to the
displacement of lower income residents out of those neighbourhoods
because of the influx of wealthier residents (Hammel, D.J.,
2009). On one hand, gentrification can increase social and ethnic
diversity as it attracts immigration which is associated with the
arrival of new residents with racial and ethnic diversity. Additionally,
for cities which have historically experienced high levels of
immigration, the diverse populations already integrated which makes
gentrification less disruptive and more likely to maintain diversity
(Hwang, J., 2015). On the other hand, gentrification can also
hinder social diversity in gentrified areas where rising property values
and living costs force original lower-income residents to be displaced.
Hence, there will be low levels of ethnic diversity in the gentrified
area while peripheral districts will experience an inflow of ethnically
diverse immigrants which can increase social diversity (Richardson,
J., Mitchell, B. and Franco, J., 2019).
Economic vitality will likely increase with income as higher-income
areas tend to have more businesses, investment, and job opportunities.
This can be explained by the multiplier effect which is as a growth pole
theory that rely on the idea that a change (increase or decrease) of one
type of economic activity in a given area leads to a change in demand
for goods and services, triggering the development of other types of
economic activity in the same area (Domański, B. and Gwosdz, K.,
2010). In the case of income, areas with higher levels of income
means residents have more disposable income which in turn increases
demand for goods and services. This creates a demand-driven multiplier
effect where the rise in consumption stimulates local businesses. This
creates a ripple effect of increasing consumer and businesses
confidence, encouraging businesses to expand and hire more employees
which overall enhance economic vitality.
Affordability will likely decrease with higher income and this can be
explained by the bid rent theory. This theory looks at how rent per area
varies with distance from the CBD. The idea behind this theory is that
as distance from the CBD increases the land available at lower price
diminishes (Narvaez, L., Penn, A. and Griffiths, S., 2013).
Hence, near the CBD, the land use will be for retailers and businesses
which are willing to pay a higher cost to be in the centre where there
is a higher concentration of population. However, the CBD therefore also
attracts high income households that have the disposable income to pay
for high land value and that want to be closer to the commercial and
leisure hub that represents the CBD. But since the bid rent will be more
expensive it will take up a proportionally greater amount of the
household’s disposable income which leads to lower housing
affordability. The opposite occurs in areas farther away from the CBD
where the rent per area falls.
Hypothesis 2- Livability will increase when environmental
quality is higher.
Livability being a socio-economic concept, both the economic and social
aspects need to be compared to the level of environmental quality.
The environmental Kuznets curve (EKC), shown in Fig.4, is a graphical
representation of the relationship between environmental degradation and
economic growth. The inverted U-curve indicates that environmental
degradation first increases as an area experiences economic growth until
a turning point where it eventually starts improving. This can be caused
by technological improvement. Additionally, as income rises, the demand
for environmental quality tends to increase as people are more willing
to prioritize and invest in a cleaner environment (Farzin, Y.H. and
Bond, C.A., 2006). Hence, we can assume that when livability rises,
economic vitality with it, there will likely be an increase in
environmental quality.
The concept of the sustainable urban development model also
demonstrates the interconnectedness between environmental quality and
livability. This model uses Egan Wheel to measure how sustainable a
community is based on socio-economic and environmental indicators. One
of the indicators focuses on environmental quality and shows how
increasing green spaces, improving air and water quality, or promoting
sustainable transportation will directly contribute to urban dwellers’
health and overall quality of life now and for future generations. For
example, clean air reduces the risk of respiratory illnesses or
sustainable transportation like walking paths or bike lanes increase
accessibility (Gough, M.Z., 2015).
Method and Investigation
Methodology
To explore the relationship between socioeconomic indicators of
livability and environmental quality, a stratified sampling strategy was
used to ensure comprehensive coverage of Valencia’s 19 districts with
varying socioeconomic profiles. In the context of a spatial analysis of
Valencia, stratified sampling was used as it allows a balanced
representation of the different districts and census sections of the
urban area. In addition, the stratification consisted of dividing the
census sections of Valencia (subunits within the different districts)
into 3 groups of low, medium, high income brackets to ensure a selection
of districts with varying socioeconomic characteristics. To generate
this stratified map, secondary data on Average income indicators in the
different census sections of Valencia was downloaded on the National
Statistics Institute of Espana (INE, 2015) and a shapefile of
Valencia was downloaded from the Ajuntament de Valencia website
(Opendatasoft.com, 2024). All data was then imported and
transformed on the computer program RStudio to generate the map seen on
Fig.5. Note that the District Pobles del Nord and Pobles del
Sud have been excluded from the sampling because of their remoteness
from Valenica’s CBD. Given the urban focus of this investigation, this
exclusion would ensure that the analysis is centered on areas where
urban socioeconomic dynamics are most relevant.
Then 3 districts from each income bracket were selected using random
sampling. Random sampling was used as it reduces subjectivity in the
selection of the districts to avoid bias and is an effective method of
sampling for large populations like the city of Valencia (Royal
Geographical Society, 2023).
District selected: Ciutat Vella (District 1), L’Eixample (District
2), El Pal del Real (District 6), Patraix (District 8), Camins al Grau
(District 12), Algiros (District 13), Quatre Carreres (District 10),
Poblats Maritims (District 11), Rascanya (District 15).
For each district both primary and secondary data was collected as
indicators to measure livability and environmental quality around
Valencia. For any primary data collection, 3 census sections within each
district were randomly selected and the measurements were averaged to
give representative data of the district as whole.
Measuring Affordability
The rent-to-income ratio is an effective way to measure housing
affordability as it shows what percentage of a household’s income is
used for rent. It is calculated using the following formula (it is
usually calculated monthly but given the limited availability of the
data it was calculated at an annual level of the year 2022 in the case
of this investigation):
Rent-to-Income Ratio =average annual rentaverage annual income100
If the rent-to-income ratio is below 30% housing is considered
affordable but the affordability threshold indicates that above 30% is
considered a rent burden and housing may be unaffordable for the average
person (www.rentspree.com). To measure the rent-to-income ratio
two data sets were used: average income in euros in 2022 (INE,
2015) and monthly rental prices per m2 in euros in 2022
(Ine.es, 2025). Then the total monthly rent was calculated by
multiplying the per m2 rent by the average apartment size which is
around 90m2 in Valencia (INE). Comparing those two metrics, the
rent-to-income ratio was then calculated shown in Fig.6.
According to Fig.6, we can observe that the districts in the
highest income bracket (Ciutat Vella, l’Eixample, and El Pla del Real)
are also the districts with the rent-to-income ratio is the lowest below
the 30% affordability threshold. This indicates that those districts are
the most affordable, specifically in L’eixample with the lowest ratio of
21.3%. All other districts are above the 30% ratio which indicates
housing may be unaffordable for the average person, specifically in
Rascanaya which reaches the highest ratio of 37.2%. Hence, the graph
does not support the hypothesis that affordability decreases with
income. However, this can be explained because high-income residents can
afford higher rent, but rental prices are not proportionally high and so
rent takes up a smaller percentage of their income. For instance, while
Ciutat Vella has a relatively high average annual rent of €4860, it is
offset by an even higher average annual income of €20593, making rent
more manageable for its residents. Hence, even if the absolute cost of
rent follows the expected trend, with higher-income districts having
higher rental prices, affordability does not follow the same pattern.
Instead, high-income districts have a greater level of affordability
because rent represents a smaller financial burden proportionally to
income. Furthermore, other factors could affect the data like the
provision of public housing. Since public housing is subsidized by
public funds and provided to people on low incomes, it provides a rental
option which is more affordable than the market rate. Hence, this can
contribute to higher levels of housing affordability. Studied on the
spatial distribution of public housing in Valencia indicates that the
district with the highest number of public housing buildings is Ciutat
Vella with 34 buildings (Gallego-Valadés, A., Ródenas-Rigla, F. and
Garcés-Ferrer, J., 2021). This coincides with the finding that
Ciutat Vella, despite having a higher average income, also exhibits a
lower rent-to-income ratio compared to other districts. Furthermore,
income elasticity of demand can be considered. Since demand for housing
is more inelastic for higher-income households living in Ciutat Vella,
L’Eixample, and El Pl del Real, they are less sensitive to changes in
rent prices which contributes to the lower rent-to-income ratios
observed in these districts.
Measuring Social Diversity
Two aspects of social diversity were measured inducing ethnic and
income diversity. Hence, the inductors choosed where the Gini
coefficient which is often used as a measure of income diversity
(Taylor, D) and the percentage of the population with
non-Spanish nationality. Both datasets were collected on the National
Statistics Institute website (INE, Population by gender and country
of birth, 2021)(INE, Gini Index and Income Distribution, 2015). The
results are presented as a scatter graph on fig.7 in order to
display the relationship between the two indicators and identify
districts that may have varying levels of social integration or
segregation.
According to Fig.7, we can observe that there is no clear
correlation between the percentage of non-spanish and Gini coefficients.
Some districts like Patraix display low levels of both non-spanish and
Gini coefficients down to 10.8% and 28.9% respectively. Other districts
like Ciutat Vella show a diverging case where both indicators are the
highest up to 20.7% of non-spanish residents and 38.2% for the Gini
coefficient. For the rest of the districts, there is only little
fluctuation in terms of the Gini coefficients which remains in a range
of 4.1% but varying levels of non-spanish residents which are
particularly high in districts from the lowest income bracket (Rascanya,
Poblats Maritims, and Quatre Carres). For example the percentage of
non-spanish residents goes up to 19.5% in Rascanya. The only anomaly is
Camins al Grau which also has a relatively high level of non-spanish up
to 16.7% while being from the middle income bracket group but this could
be explained by the relatively high level of affordability (Camins al
Grau being just under the 30% affordability threshold) which can attract
a more diverse demographic. Overall, if we look at the districts from
the higher income bracket (Ciutat Vella, l’Eixample, and El Pla del
Real), we observe that the level of non-Spanish population varies in
each case. This supports the hypothesis that within higher income
brackets, whether social diversity increases or decreases does not
follow a clear trend. This can be explained by historical migration
patterns which have a significant impact on the level of social and
ethnic diversity. For example, Ciutat Vella is one of the oldest areas
of Valencia and acts as a historic center (Pérez, R.M. and Pérez,
R.M., 2017). Hence, the area has attracted a diverse demographic of
foreign workers and tourists from varying ethnic backgrounds (Álvaro
Mazorra Rodríguez, 2024). Furthermore, in 1992, Valencia signed the
Plan Integral de Rehabilitación de Valencia (Plan RIVA) to solve the
issue of urban degradation. This consisted of constructing new
facilities for social and educational purposes as well as public housing
which successfully contributed to the rehabilitation of Ciutat Vella
(Aq.upm.es., 2025) and further stimulated the arrival of new
residents and tourists. However, the gentrification that resulted from
the project also caused elderly, low income immigrants, and other
marginalized groups to be displaced to peripheral districts. Hence, this
can explain why districts that are relatively far away from the city
center like Rascanya, Quatre Carres, or Poblats Maritims have higher
levels of non-spanish residents 19.5%, 16.0%, and 16.8% respectively.
The main anomaly is Patraix which displays a low 10.8% of non-spanish
residents while being relatively far from Ciutat Vella. This can be a
result of Patraix neighborhoods being more residential and therefore
attracting families and long-term residents which contributes to a
stable but potentially less diverse population.
The two metrics were then used to calculate a composite indicator of
social diversity. Both indicators where normalized then the social
diversity index for each district was calculated by adding both
normalized indicators (equal weighting was used as it is assumed that
both indicators have equal importance).
The results are displayed in Fig.9 as a bar chart showing
the level of social diversity by districts. Once again, we can observe
that there is no particular trend between the social diversity index and
income bracket as the three districts with the highest social diversity
score all belong to different income brackets. For example, Ciutat Vella
has the highest score of social diversity of 1.00 because of mixed
incomes and the significant presence of foreign residents. To the other
extreme Patraix has a score of 0.04 which indicates very low social
diversity.
Measuring Accessibility
To measure accessibility, the focus was around three main facilities
encompassing schools (including public schools, charter schools, and
private schools), hospitals, and public transport facilities as those
are key indicators of urban connectivity and accessibility. All data was
collected secondarily on the Ajuntament de Valencia website
(Educational Centers in Valencia, 2022)(Hospitales y otros centros
sanitarios, 2024)(Public Transport Stations, 2022). For a spatial
representation of the data, the distribution of schools, hospitals, and
transport stations are displayed on Fig.10. In terms of
schools, we can observe that the two districts with the greatest number
of schools are Poblats Maritims with 34 schools and Quatre Carres with
32 schools which are also the districts with the greatest total surface
area of 9783km² and 11325km² respectively (Pérez, R.M. and Pérez,
R.M., 2017). Inversely, the smallest district in surface area,
Ciutat Vella which is 169km², is also the district with the lowest
number of schools down to 15 schools. This same pattern follows for the
number of hospitals and public transport facilities where Quatre Carres
have up to 6 hospitals and 153 public transport stations while Ciutat
Vella only has 1 hospital and 49 public transport stations. Hence, the
general trend is that the greater the surface area the greater the
number of facilities. However, this becomes less significant to measure
accessibility as a higher number of facilities does not necessarily mean
they are evenly distributed or easily reachable for all urban
dwellers.
Hence, for a more significant evaluation of accessibility, facilities
per km² was also measured for a focus on spatial availability adjusted
to surface area variation between districts. Results are displayed on
Fig.11 which shows the number of facilities per km² in each
district. We can observe that the two districts with the highest level
of public transport station per km² are Ciutat Vella and L’Eixample with
0.29 stations/km² and 0.04 stations/km² respectively. On the other hand,
Poblats Maritims and Quatre Carres have the lowest level of public
transport down to 0.01 stations/km² for both districts. Hence, the
general trend shows that districts from the higher income bracket
(Ciutat Vella, l’Eixample, and El Pla del Real) also have a greater
public transport station density and hence a greater level of
accessibility and vice versa with districts from the lowest income
bracket (Poblats Maritims, Quatre Carres, and Rascanya). This can be
explained using the monocentric city model which demonstrates how land
use and property values change relative to distance from the CBD. Hence,
according to the model, Ciutat Vella which is the historic center of
Valencia and L’Eixample which acts as the primary CBD will naturally
have a greater public transport density as these areas have the highest
demand for accessibility. This is because the concentration of
businesses, services, and economic activity in the CBD creates a
stronger incentive for investment in transport infrastructure which in
turn increases accessibility (Bentlage, M., Müller, C. and
Thierstein, A., 2020). Furthermore, the pattern is the same with
school and hospital density which are the highest in Ciutat Vella with
0.089 schools/km² and 0.0059 hospital/km² while being the lowest in
Quatre Carres down to 0.003 schools/km² and 0.0005 hospital/km². This is
because, according to the central place theory, higher-order services
like hospitals and schools (specially universities) are usually
concentrated in large urban centers in districts like Ciutat Vella
(King, L., 2020). Hence, with no notable anomalies, the data
supports the first hypothesis that accessibility increases in the CBD
where the higher income bracket districts are located.
Measuring Economic Vitality
To measure economic vitality two matrices were used including average
income per person and unemployment level. Both dataset was collected
secondarily (INE, Indicadores de renta media y mediana,
2015)(Informe situación socio-laboral de la ciudad de València Datos
básicos del mundo del trabajo, 2021). Both indicators were then
normalized and combined to create a composite indicator of economic
vitality. The standardized unemployment rate was subtracted from 1 as
this indicator contributes negatively to economic vitality.
The economic vitality scores are displayed for each district as a bar
chart, shown in Fig.13, to facilitate easier comparison. We can
observe that the data support the hypothesis that economic vitality
increases with income as the three districts with the highest economic
vitality score are also the districts from the highest income bracket.
This includes El Pla del Real with a score of 1.00, and L’Eixample and
Ciutat Vella with an equal score of 0.89. Conversely, Rascanya, Poblats
Maritims, and Quatre Carreres are the districts from the lowest income
bracket and have the lowest economic vitality score down to 0.15, 0.08,
and 0.07 respectively. There is no notable anomaly, though we can note
that the economic vitality score of Camins al Grau is relatively lower
down to 0.28 despite having moderate income levels. This is because of
the high unemployment rate of 8.1% in Camins al Grau which brings the
economic vitality score downard. Overall, this trend can be explained by
the multiplier effect in districts like Ciutat Vella and L’Eixample
which experience high consumer and businesses confidence due to high
income level and the commercial and economic hub that the CBD represents
(Valencia.es., 2017). This further contributes to the low
unemployment rate in both Ciutat Vella and L’Eixample which is only 2.6%
and 3.6% respectively, boosting economic vitality.
Measuring Environmental Quality
Different formats of environmental quality surveys exist, but in the
case of this investigation 4 essential factors of environmental quality
were assessed: cleanness, level of pollution, access to green space, and
aesthetic appeal. For each factor, multiple indicators were graded on a
scale of quality from 1 to 4. To measure the environmental quality
across Valencia, a total of 27 environmental quality surveys were
conducted on the 11 of january 2025. This includes 3 environmental
quality surveys in 3 different census sections randomly selected within
each district in order to have a more representative measure of
environmental quality of the districts as a whole. The geographical
location of each site where data was collected is shown on
Fig.14. Since the investigation focuses on spatial comparison,
no repeat was carried out when assessing environmental quality.
Fig.14: Map showing geographical point of data collection in
Valencia (shown by the red dots)
A majority of the indicators were assessed visually except from air
quality and noise pollution that were measured using digital tools. For
air quality the level of carbon dioxide was recorded with a digital Co2
monitor and for noise pollution decibel level was recorded using a
decibel meter app. In both cases, 5 repeats were taken each time and an
average was calculated in order to have more reliable data and when Co2
or decibel was measured the recording tool was kept at the same distance
from the floor each time. Finally, so that the data could be compared
with the rest of the environmental survey, measurements were converted
into a standardized scale using the following equation.
Scaled Value = 1 + (X - Xmin) (4 - 0)Xmax - Xmin
The results of the environment survey are displayed as a radial line
graph for each district shown by Fig.15. For each district, the
indicator scores are averaged across the three survey locations which
aim to reduce variability from individual survey locations. Note that
the scoring is consistent: a score of 1 indicates a low level of the
given indicator (whether positive or negative), while a score of 4
indicates a high level.
The results of the environmental quality surveys show the variations
in environmental quality across the different districts of Valencia.
Districts with the highest level of environmental quality overall is
Ciutat Vella which scores a maximum score of 4 in three different
indicators including air quality, pathway maintenance, and budding
design. This is because Ciuata Vella is the historical centre of
Valencia and is therefore characterised by historic architecture and
pedestrianized areas. The only indicator that lowers the environmental
quality of Ciutat Vella is noise pollution which is relatively high up
to 2.4 and can be attributed to high foot traffic owing to commercial
and cultural land use of the district which can attract a lot of
tourists and residents. With the second highest environmental quality
comes Quatre Carreres which has a high score of 3 for green space cover
and 3.8 for pathway maintenance which can be explained because Quatre
Carreres has the largest section of the Turia Gardens, the largest urban
park in Spain (Lovell, L., 2025). However the district has a
relatively high level of air pollution and graffiti with a score of 2.4
and 2.2 respectively which contributes to an overall lower level of
environmental quality. Conversely, Rascanya has the lowest environmental
quality with a score of 0.0 for presence of art but a relatively high
score of 2.7 for graffiti. The only indicator that contributes to
increasing the environmental quality in Rascanay is the relatively high
score of 2.4 for green space cover. Overall, the trend we can observe is
that central districts like Ciutat Vella and El Pla del Real tend to
score higher in aesthetic appeal and access to green space aspects of
environmental quality but struggle with issues of cleanness and of
pollution. On the other hand, more peripheral districts like Rascanya
and Poblats Maritims tend to have lower environmental quality because of
higher levels of graffiti and a lack of cultural elements but also
display high levels of green space. This trend can be explained because
central areas generally receive more investment in urban aesthetics and
pedestrian infrastructure because of tourism and economic activity.
Spatial Analysis
Fig.17 shows spatial variations in environmental quality and
livability. Looking at wider spatial patterns, we observe that the
coastal area of Valencia has a low level of livability relative to
inland areas of the city, particularly in terms of accessibility and
economic vitality. For example, Poblats Maritims which extended on the
coast of the mediterranean sea display low levels of accessibility and
economic vitality down to 0.00 and 0.08 respectively. However, the
coastal area also demonstrates a medium-to-high level of environmental
quality up to 0.49 caused by the higher levels of tourism-driven
conservation efforts which leads to high pathway maintenance (score of
3.4 on the environmental survey). Meanwhile, inland districts like
Ciutat Vella display both high levels of livability and high
environmental quality and this can be explained by the environmental
Kuznets curve. Ciutat Vella has a high level of economic vitality of
0.89 which has caused residents of the district to advocate for greater
investment in environmental protection and urban planning, hence
increasing environmental quality over time. Many projects have been put
in place in Ciutat Vella to improve the environmental quality like the
restoration of the Plaza del Ayuntamiento and the creation of
pedestrian-only zones that improve air quality and reduce noise
pollution (Valencia.es., 2025).
Social Diversity
Social diversity is defined as the variety of people in an area, including differences in income levels, ethnicities, or age group (Talen, E. and Lee, S., 2018). Social Diversity contributes to urban livability as it enhances economic vitality by promoting creativity among individuals and businesses. Exposure to diverse backgrounds, experiences, and perspectives will stimulate innovation and boost the overall creative and innovative capacity of human capital (Moroni, S., 2016).